Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments

This paper presents two novel control methodologies for the cooperative manipulation of an object by N robotic agents. Firstly, we design an adaptive control protocol which employs quaternion feedback for the object orientation to avoid potential representation singularities. Secondly, we propose a control protocol that guarantees predefined transient and steady-state performance for the object trajectory. Both methodologies are decentralized, since the agents calculate their own signals without communicating with each other, as well as robust to external disturbances and model uncertainties. Moreover, we consider that the grasping points are rigid, and avoid the need for force/torque measurements. Load distribution is also included via a grasp matrix pseudo-inverse to account for potential differences in the agents' power capabilities. Finally, simulation and experimental results with two robotic arms verify the theoretical findings

AI based Robot Safe Learning and Control

Introduction This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities

Design, Control and Motion Planning for a Novel Modular Extendable Robotic Manipulator

This dissertation discusses an implementation of a design, control and motion planning for a novel extendable modular redundant robotic manipulator in space constraints, which robots may encounter for completing required tasks in small and constrained environment. The design intent is to facilitate the movement of the proposed robotic manipulator in constrained environments, such as rubble piles. The proposed robotic manipulator with multi Degree of Freedom (m-DOF) links is capable of elongating by 25% of its nominal length. In this context, a design optimization problem with multiple objectives is also considered. In order to identify the benefits of the proposed design strategy, the reachable workspace of the proposed manipulator is compared with that of the Jet Propulsion Laboratory (JPL) serpentine robot. The simulation results show that the proposed manipulator has a relatively efficient reachable workspace, needed in constrained environments. The singularity and manipulability of the designed manipulator are investigated. In this study, we investigate the number of links that produces the optimal design architecture of the proposed robotic manipulator. The total number of links decided by a design optimization can be useful distinction in practice. Also, we have considered a novel robust bio-inspired Sliding Mode Control (SMC) to achieve favorable tracking performance for a class of robotic manipulators with uncertainties. To eliminate the chattering problem of the conventional sliding mode control, we apply the Brain Emotional Learning Based Intelligent Control (BELBIC) to adaptively adjust the control input law in sliding mode control. The on-line computed parameters achieve favorable system robustness in process of parameter uncertainties and external disturbances. The simulation results demonstrate that our control strategy is effective in tracking high speed trajectories with less chattering, as compared to the conventional sliding mode control. The learning process of BLS is shown to enhance the performance of a new robust controller. Lastly, we consider the potential field methodology to generate a desired trajectory in small and constrained environments. Also, Obstacle Collision Avoidance (OCA) is applied to obtain an inverse kinematic solution of a redundant robotic manipulator

Task-space dynamic control of underwater robots

This thesis is concerned with the control aspects for underwater tasks performed by marine robots. The mathematical models of an underwater vehicle and an underwater vehicle with an onboard manipulator are discussed together with their associated properties. The task-space regulation problem for an underwater vehicle is addressed where the desired target is commonly specified as a point. A new control technique is proposed where the multiple targets are defined as sub-regions. A fuzzy technique is used to handle these multiple sub-region criteria effectively. Due to the unknown gravitational and buoyancy forces, an adaptive term is adopted in the proposed controller. An extension to a region boundary-based control law is then proposed for an underwater vehicle to illustrate the flexibility of the region reaching concept. In this novel controller, a desired target is defined as a boundary instead of a point or region. For a mapping of the uncertain restoring forces, a least-squares estimation algorithm and the inverse Jacobian matrix are utilised in the adaptive control law. To realise a new tracking control concept for a kinematically redundant robot, subregion tracking control schemes with a sub-tasks objective are developed for a UVMS. In this concept, the desired objective is specified as a moving sub-region instead of a trajectory. In addition, due to the system being kinematically redundant, the controller also enables the use of self-motion of the system to perform sub-tasks (drag minimisation, obstacle avoidance, manipulability and avoidance of mechanical joint limits)

Control of Nonlinear Mechatronic Systems

This dissertation is divided into four self-contained chapters. In Chapter 1, an adaptive nonlinear tracking controller for kinematically redundant robot manipulators is presented. Past research efforts have focused on the end-effector tracking control of redundant robots because of their increased dexterity over their non-redundant counterparts. This work utilizes an adaptive full-state feedback quaternion based controller developed in [1] and focuses on the design of a general sub-task controller. This sub-task controller does not affect the position and orientation tracking control objectives, but instead projects a preference on the configuration of the manipulator based on sub-task objectives such as the following: singularity avoidance, joint limit avoidance, bounding the impact forces, and bounding the potential energy. In Chapter 2, two controllers are developed for nonlinear haptic and teleoperator systems for coordination of the master and slave systems. The first controller is proven to yield a semi-global asymptotic result in the presence of parametric uncertainty in the master and the slave dynamic models provided the user and the environmental input forces are measurable. The second controller yields a global asymptotic result despite unmeasurable user and environmental input forces provided the dynamic models of the master and slave systems are known. These controllers rely on a transformation and a flexible target system to allow the master system\u27s impedance to be easily adjusted so that it matches a desired target system. This work also offers a structure to encode a velocity field assist mechanism to provide the user help in controlling the slave system in completing a pre-defined contour following task. For each controller, Lyapunov-based techniques are used to prove that both controllers provide passive coordination of the haptic/teleoperator system when the velocity field assist mechanism is disabled. When the velocity field assist mechanism is enabled, the analysis proves the coordination of the haptic/teleoperator system. Simulation results are presented for both controllers. In Chapter 3, two controllers are developed for flat multi-input/multi-output nonlinear systems. First, a robust adaptive controller is proposed and proven to yield semi-global asymptotic tracking in the presence of additive disturbances and parametric uncertainty. In addition to guaranteeing an asymptotic output tracking result, it is also proven that the parameter estimate vector is driven to a constant vector. In the second part of the chapter, a learning controller is designed and proven to yield a semi-global asymptotic tracking result in the presence of additive disturbances where the desired trajectory is periodic. A continuous nonlinear integral feedback component is utilized in the design of both controllers and Lyapunov-based techniques are used to guarantee that the tracking error is asymptotically driven to zero. Numerical simulation results are presented for both controllers. In Chapter 4, a new dynamic model for continuum robot manipulators is derived. The dynamic model is developed based on the geometric model of extensible continuum robot manipulators with no torsional effects. The development presented in this chapter is an extension of the dynamic model proposed in [2] (by Mochiyama and Suzuki) to include a class of extensible continuum robot manipulators. First, the kinetic energy of a slice of the continuum robot is evaluated. Next, the total kinetic energy of the manipulator is obtained by utilizing a limit operation (i.e., sum of the kinetic energy of all the slices). Then, the gravitational potential energy of the manipulator is derived. Next, the elastic potential energy of the manipulator is derived for both bending and extension. Finally, the dynamic model of a planar 3-section extensible continuum robot manipulator is derived by utilizing the Lagrange representation. Numerical simulation results are presented for a planar 3-section extensible continuum robot manipulator

Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach

Previous works of traditional zeroing neural networks (or termed Zhang neural networks, ZNN) show great success for solving specific time-variant problems of known systems in an ideal environment. However, it is still a challenging issue for the ZNN to effectively solve time-variant problems for uncertain systems without the prior knowledge. Simultaneously, the involvement of external disturbances in the neural network model makes it even hard for time-variant problem solving due to the intensively computational burden and low accuracy. In this paper, a unified neural approach of simultaneous identification, tracking control and disturbance rejection in the framework of the ZNN is proposed to address the time-variant tracking control of uncertain nonlinear dynamics systems (UNDS). The neural network model derived by the proposed approach captures hidden relations between inputs and outputs of the UNDS. The proposed model shows outstanding tracking performance even under the influences of uncertainties and disturbances. Then, the continuous-time model is discretized via Euler forward formula (EFF). The corresponding discrete algorithm and block diagram are also presented for the convenience of implementation. Theoretical analyses on the convergence property and discretization accuracy are presented to verify the performance of the neural network model. Finally, numerical studies, robot applications, performance comparisons and tests demonstrate the effectiveness and advantages of the proposed neural network model for the time-variant tracking control of UNDS